Historically, people interested in learning about weather conditions relating to a particular area via portable wireless devices have had few options in terms of the granularity of result, and thus also in terms of how relevant the results are to the user. Anybody who has viewed a television or radio broadcast in a metropolitan area has experienced this shortcoming. For instance, a broadcast listener living in a suburb of Philadelphia may listen to a weather broadcast concerning the Philadelphia metropolitan area, but nonetheless receive a report that is not very relevant to the listener. For example, while the news station may report heavy snow of 8-12″ for “center city”, the particular Philadelphia suburb, in which the listener may reside or through which the listener may be commuting, may be receiving little or no snow at all. The problem compounds when one considers the number of listeners that reside in rural areas that are disconnected from the weather patterns of the greater metropolitan regions at large. In short, network news broadcasts are not granular enough to simultaneously describe relevant results to all of its viewers.
Thus, there is a need for a simple way for people interested in learning about weather conditions in a more relevant, localized way to have access to such information. Today, existing wireless devices can obtain weather information online from the world wide web, or the Internet, but these sources too are not as rich or localized as would be desired to avoid the presentation of potentially irrelevant weather information to a consumer of the weather information. Oftentimes, such Internet sites and services (e.g., www.weather.com, www.accuweather.com, Yahoo! Weather, etc.) ask the user to enter a zip code, or city location, to specify an area for the desired weather information. While the presentation of results based on a zip code may be adequate to some users to describe the weather conditions prevailing in certain metropolitan locations, where zip codes tend to be defined densely, such an approach may translate poorly to a user residing or passing through a rural location, where zip codes may be defined broadly to encompass a variety of different weather conditions at any one time.
The problem lies at least partially in the way the National Weather Service has historically reported weather data. In this regard, the National Weather Service observational grid, depicted in exemplary fashion in FIG. 1, consists primarily of “METAR” sites (“METAR” roughly translated into English from French stands for Aviation Routine Weather Report), typically located at airports at a nominal spacing of ˜25 square miles (only the cyan colored squares are METAR stations). The data for FIG. 1 was retrieved online from the National Weather Service's online source (http://www.wrh.noaa.gov/sew/newsea.php). In short, even if a user does have access to packet based services from a portable device, the weather data that is exposed online is not granular enough to always be meaningful.
Alternate techniques for directly measuring local weather environments in a more immediate and relevant/accurate fashion are thus desirable. It would be further desirable to provide versatility, connectivity, and accuracy of weather condition reporting via wireless mobile devices or similar portable devices. It would be further desirable to provide a service based on such weather condition reporting data to provide accurate localized data to portable device users. For users that do not have packet switch services (e.g., TCP/IP packet services) or access to other online services with their portable device, it would be advantageous to provide the above-described benefits to enable the delivery of weather reporting data because no alternatives, however slow or granular, exist for such users. It would be still further desirable to improve upon control/voice interfaces and infrastructures for portable wireless devices to enable the delivery of periodic measurements from a server node within a network to end users within the network based on their respective locations relative to nearest network towers.